Eco-Access? Investigating archival documentation for presence and radical re-definitions of access, inclusivity, and disability representation
Bibliographic record
Abstract
My research is focused on analyzing archival documents to highlight inclusive language and themes of disability representation. Access in this analysis is seen on a sliding scale, as nature and the natural world, when analyzed through the lens accessibility is not simply a binary with the urban. Approaching the research, I reflected on and used a critical disability-oriented lens, with a specific focus on eco-crip theory to better understand access and inclusion of disability in urban natural spaces. I am interested in how the perception of access to nature affects the dialogue surrounding representation, and as a by-product, the experience individuals with physical disabilities may have in wilderness and nature. Experience in this context is posited on, first, the representation in the environment and second, the movement and act of accessing or moving through the space. For this research, I chose to investigate archival content documenting the Leslie Street Spit, a unique urban area in Toronto, Ontario. My archival investigation involved highlighting and interpreting the key themes of access, disability, and nature, using the Spit as an example, to find representation and presence of individuals with physical disabilities. Through the research, I discuss and argue that access and inclusion ought to be interpreted on a sliding scale, particularly when attempting to integrate these themes into discussions of nature and the environment. With this in place, the use of language and representation can lead to creating more inclusive spaces in the outdoors, both physically and theoretically. I suggest an increasing need to acknowledge and promote the presence of a diverse range of beings in nature and adjust the current assumptions of access and inclusion that tend to exclude disability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.032 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.017 | 0.042 |
| Scholarly communication | 0.023 | 0.021 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".